US11719766B2 - Magnetic distribution detection method - Google Patents

Magnetic distribution detection method Download PDF

Info

Publication number
US11719766B2
US11719766B2 US17/585,570 US202217585570A US11719766B2 US 11719766 B2 US11719766 B2 US 11719766B2 US 202217585570 A US202217585570 A US 202217585570A US 11719766 B2 US11719766 B2 US 11719766B2
Authority
US
United States
Prior art keywords
magnetic
sample
detection method
magnetic distribution
distribution detection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active, expires
Application number
US17/585,570
Other versions
US20230078584A1 (en
Inventor
Yi-Chun Chen
Yi-De LIOU
Guo-Wei Huang
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
National Cheng Kung University NCKU
Original Assignee
National Cheng Kung University NCKU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by National Cheng Kung University NCKU filed Critical National Cheng Kung University NCKU
Assigned to NATIONAL CHENG KUNG UNIVERSITY reassignment NATIONAL CHENG KUNG UNIVERSITY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CHEN, YI-CHUN, HUANG, GUO-WEI, LIOU, YI-DE
Publication of US20230078584A1 publication Critical patent/US20230078584A1/en
Application granted granted Critical
Publication of US11719766B2 publication Critical patent/US11719766B2/en
Active legal-status Critical Current
Adjusted expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q60/00—Particular types of SPM [Scanning Probe Microscopy] or microscopes; Essential components thereof
    • G01Q60/50—MFM [Magnetic Force Microscopy] or apparatus therefor, e.g. MFM probes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/02—Measuring direction or magnitude of magnetic fields or magnetic flux
    • G01R33/038—Measuring direction or magnitude of magnetic fields or magnetic flux using permanent magnets, e.g. balances, torsion devices
    • G01R33/0385—Measuring direction or magnitude of magnetic fields or magnetic flux using permanent magnets, e.g. balances, torsion devices in relation with magnetic force measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q30/00—Auxiliary means serving to assist or improve the scanning probe techniques or apparatus, e.g. display or data processing devices
    • G01Q30/04—Display or data processing devices
    • G01Q30/06—Display or data processing devices for error compensation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q60/00—Particular types of SPM [Scanning Probe Microscopy] or microscopes; Essential components thereof
    • G01Q60/02—Multiple-type SPM, i.e. involving more than one SPM techniques
    • G01Q60/08—MFM [Magnetic Force Microscopy] combined with AFM [Atomic Force Microscopy
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/12—Measuring magnetic properties of articles or specimens of solids or fluids
    • G01R33/1292—Measuring domain wall position or domain wall motion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present invention relates to a magnetic distribution detection method for detecting the magnetic distribution of a sample, particularly to the magnetic distribution detection method using machine learning.
  • Magnetic Force Microscope is an atomic force microscope that uses a magnetic probe to scan a magnetic sample, and the interaction between a testing probe and the surface of a magnetic sample to measure the magnetic distribution or magnetic structure of the surface of the magnetic sample.
  • MFM Magnetic Force Microscope
  • the measuring distance from the sample is closer for measuring the influence caused by the van der Waals force.
  • the measuring distance from the sample is farther, primarily for detecting the magnetic force. The results of the two measurements are combined to obtain the magnetic distribution of the surface of the sample.
  • the measuring distance from the surface of the sample in the first measurement is closer, so that the result of the measurement is influenced by the van der Waals force significantly, and it is not easy to know about the magnetic force distribution at a closer distance.
  • the measuring distance from the surface of the sample is farther, the obtained magnetic signal is weaker because the magnetic force is inversely proportional to the square of distance and the spatial resolution of the magnetic distribution is limited because the diverging magnetic lines of each magnetic domain are mixed together.
  • the magnetic distribution detection method of the present invention is used for detecting the magnetic distribution of a sample, and the method includes the steps of (a) providing a magnetic sensor, and aligning a probe of the magnetic sensor precisely with the sample; (b) selecting a plurality of measuring points on the sample; (c) sensing the measuring points by the magnetic sensor, and obtaining a plurality of sense data; (d) converting the sense data into a plurality of data groups by a signal decomposition algorithm; and (e) selecting one of the data groups as the magnetic distribution data of the sample.
  • step (c) a plurality of heights (distances) from the measuring points is sensed, and the sense datum corresponding to each height is obtained.
  • the magnetic sensor is a magnetic force microscope.
  • the signal decomposition algorithm is a principal component analysis algorithm.
  • the method further comprises the steps of (f) selecting a measuring point in a different magnetic area on the surface of the sample and (g) analyzing the relationship between the heights above the selected measuring points and the data obtained in the step (e).
  • the step of clustering the magnetic distribution sense data by a clustering algorithm is carried out between the step (e) and the step (f).
  • the clustering algorithm is a k-means clustering algorithm.
  • the height has a range of 0 ⁇ 50 nm or 50 ⁇ 250 nm.
  • the step of clustering the magnetic distribution sense data by a clustering algorithm is carried out after the step (e).
  • the clustering algorithm is a k-means clustering algorithm.
  • the sense data are represented by a phase difference.
  • FIG. 1 A is a schematic view of a magnetic force microscope used in a magnetic distribution detection method in accordance with an embodiment of the present invention
  • FIG. 1 B is a schematic view of a probe measuring a sample at a higher position
  • FIG. 2 A is a flow chart of a magnetic distribution detection method of the present invention
  • FIG. 3 A is a top view of a sample
  • FIG. 3 B is a schematic view of the phase difference ⁇ and the height ⁇ Z when the distance of the magnetic probe from a surface of a sample 9 is the height ⁇ Z;
  • FIGS. 4 A to 4 C are pictures showing the force exertion distribution of the first to third data groups analyzed by principal component analysis respectively;
  • FIGS. 5 A and 5 B are pictures showing the magnetic distribution when the range of the heights above measuring points is set to 50 ⁇ 250 nm for a magnetic force microscope;
  • FIG. 6 is a flow chart of a magnetic distribution detection method in accordance with another embodiment of the present invention.
  • FIG. 7 is a picture showing a pattern obtained after clustering computing
  • FIG. 8 is a graph showing the relationship between phase difference ⁇ and height.
  • FIGS. 9 A and 9 B are pictures showing the magnetic distribution of a weak magnetic sample.
  • the magnetic force microscope 10 includes a cantilever 11 and a probe 12 , and the probe 12 is magnetic and installed at the front of the cantilever 11 .
  • the magnetic force microscope 10 uses the magnetic force between the probe 12 and a magnetic material (which is the sample 9 ) to measure the magnetic distribution of the sample 9 and then uses a computer system (not shown in the figure) to create an image of the magnetic distribution.
  • the probe 12 measures the sample 9 from a closer distance at first.
  • the magnetic force microscope 10 generally needs to scan the sample 9 twice.
  • the probe 12 measures the sample 9 from a closer distance (0 ⁇ 50 nm) by a tapping mode, so that the influence to the cantilever 11 caused by the van der Waals force of the sample 9 is much greater than by the magnetic force, and thus the action of the cantilever 11 mainly reflects the appearance of the surface of the sample 9 .
  • the surface of the sample 9 has a bump 91 and a groove 92 , and thus the linear graph 11 P showing the action of the cantilever 11 also reflects the appearance and shape of the bump 91 and the groove 92 .
  • the probe 12 In order to reduce the influence caused by the van der Waals force, the probe 12 needs to keep a certain distance from the sample 9 to measure the magnetic signal of the sample 9 under the influence of the van der Waals force. Specifically, it is necessary to lift the probe 12 to a position with a distance D (such as 50 ⁇ 250 nm) from the surface of the sample 9 , and then carry out the second scan along the moving path of the first scan (please refer to FIG. 1 B for a probe 12 measuring the sample 9 at a higher position). In this way, the force exertion conditions of the probe 12 obtained in the first scan and the second scan are taken into consideration to determine the magnetic distribution of sample 9 . However, in the second scan, the probe 12 measures the sample 9 from a higher position, so that the received magnetic signal is weak and divergent, and the detection accuracy and the spatial resolution are poor.
  • a distance D such as 50 ⁇ 250 nm
  • the conventional test needs to reverse the original magnetic direction of the probe 12 (for example, the original north pole facing the sample is changed to the south pole facing the sample) and measure the same magnetic domain again in order to know the magnetism of each magnetic domain.
  • the original magnetic direction of the probe 12 for example, the original north pole facing the sample is changed to the south pole facing the sample
  • the magnetic distribution detection method comprises the following steps:
  • Step S 1 Provide a magnetic sensor and a sample 9 .
  • the sample 9 is a magnetic tape; in other words, the magnetic area on the surface of the sample 9 shows a tape-shaped distribution. It is noteworthy that the magnetic area distribution on the sample 9 as depicted in FIG. 3 A is illustrative only, but it does not represent the real situation.
  • the magnetic sensor is the magnetic force microscope 10 as shown in FIG. 1 A
  • the material of the sample 9 is a ferromagnetic material with strong magnetism which is conducive to the observation of the magnetic distribution of the sample.
  • the magnetic sensor can also be a Scanning NV Magnetometry, a Scanning SQUID Microscope, or a Scanning Hall Probe Microscope (SHPM).
  • Step S 2 Select a plurality of measuring points 9 P on the sample 9 , and the so-called measuring point 9 P refers to a measuring point of the sample 9 in the magnetic force microscope 10 , and the measuring points 9 P is such as arranged in a matrix form.
  • the measuring points 9 P is such as arranged in a matrix form.
  • FIG. 3 A only 64 measuring point 9 P are shown, but more measuring points 9 P, such as those arranged in a 128*128 matrix including 16384 measuring points are generally used. People having ordinary skill in the art should understand that the more the measuring points, the higher the resolution of the image of the magnetic distribution.
  • FIG. 3 B is the schematic view of the phase difference ⁇ and the height ⁇ Z when the distance of the magnetic probe from a surface of a sample 9 is the height ⁇ Z.
  • Step S 3 Use a probe 12 of a magnetic force microscope 10 to obtain a plurality of sense data corresponding to a series of heights above the measuring points 9 P respectively.
  • the probe 12 of the magnetic force microscope 10 senses a plurality of heights above the measuring points 9 P respectively and obtains the sense data (phase difference ⁇ ) corresponding to the heights respectively.
  • the probe 12 can stay at a position of a different height (within a range of 0 ⁇ 250 nm) above each measuring point 9 P for the sensing in order to obtain the phase difference ⁇ corresponding to the different height.
  • the range of the height from 0 nm to 250 nm can be divided into 256 points.
  • the range of the height from 0 nm to 250 nm is divided into 256 equal parts, and the probe 12 is placed at each different height to measure the sample 9 and record the measurements.
  • the sample 9 is made of a ferromagnetic material having a plurality of magnetic domains. Since each magnetic domain has a different magnetic effect on the probe 12 , the magnetic force microscope 10 can be used to obtain the relative magnetic properties of the magnetic domain.
  • the sense data are represented by the phase difference ⁇ .
  • the relationship between the phase difference ⁇ and the action force along the Z-axis of the probe 12 is shown below:
  • phase difference ⁇ is equivalent to measuring the exerted magnetic force.
  • the phase difference ⁇ of each point on the same plane of the sample 9 is expressed by brightness and contrast and used to express the magnetic distribution of the surface of the sample 9 .
  • the phase difference ⁇ of each point on the sample 9 can also be expressed by a 3D drawing.
  • the Z-axis represents the phase difference ⁇
  • the XY plane represents the surface of the sample 9 .
  • the force exertion of the probe 12 can also be represented by frequency or amplitude.
  • all sense data are represented by the phase difference ⁇ .
  • the range of the height above the measuring point 9 P of the probe 12 of the magnetic force microscope 10 is set to 0 ⁇ 50 nm. Since the magnetic force is inversely proportional to the square of distance, therefore the magnetic force microscope 10 is closer to the sample 9 within this range, and the phase difference ⁇ can be measured more precisely.
  • the distance between the probe 12 of the magnetic force microscope 10 and the sample 9 can have another height range such as 50 ⁇ 250 nm.
  • Step S 4 Use a signal decomposition algorithm to convert the sense data (or phase difference ⁇ ) into a plurality of data groups.
  • the signal decomposition algorithm used in this embodiment is a principal component analysis algorithm (PCA) which is a data dimension reduction analysis technique.
  • PCA principal component analysis algorithm
  • the principal component analysis algorithm will convert all sense data into a plurality of data groups which are independent to one another in vector space.
  • the principal component analysis used in this embodiment converts all sense data into multiple data groups.
  • the number of data groups is equal to the number of height divisions (e.g. 256 points).
  • three diagrams of the force exertion distribution are formed according to the first to third data groups (the force exertion distribution of other groups are not shown in the embodiment).
  • the smaller the labelled number the principal component the larger the representativeness to the sense data.
  • the first group of principal components has a percentage of impact higher than that of the second groups of principal components
  • the second group of principal components has a percentage of impact higher than that of the third group of principal components.
  • other types of signal decomposition algorithm can also convert the sense data into the plurality of data groups, in which there is one of the groups that represents the influence caused by the magnetic force.
  • the principal component analysis algorithm is used as the signal decomposition algorithm.
  • other types of signal decomposition algorithm such as non-negative matrix factorization, independent components analysis, or factor analysis can also be used in other embodiments of the present invention.
  • the reason why the principal component analysis algorithm or other signal decomposition algorithms can be used to convert the measured signal into different components resides on the sense data of at a series of heights above the measuring points 9 P as described in the Step S 3 . Since the change of van der Waals force varying with the height and the change of the magnetic force varying with the height are different, therefore the principal component analysis can convert the van der Waals force and the magnetic force in the sense data into different data groups.
  • Step S 5 Select one of the data groups as the magnetic distribution data of the sample.
  • the sample 9 of this embodiment is a magnetic tape made of a ferromagnetic material. Since the sample 9 is a magnetic tape, the magnetic distribution will be in the shape of a tape, and the first group of principal components as shown in FIG. 4 A are able to be determined as being contributed by magnetism. Since the magnetic distribution of the magnetic tape can be expected to be in the shape of a tape, therefore in the Step S 5 a computer with function or algorithm for pattern recognition is used for determination in some embodiment.
  • FIGS. 4 B and 4 C show the surface undulation and the left half side of the particles on the surface, respectively.
  • the probe 12 of the magnetic force microscope 10 is closer to the surface of the sample 9 and the detection result is affected by the van der Waals force more easily, one of the groups (which is the first group of principal components of this embodiment) can be used as the magnetic distribution and the magnetic distribution chart can be drawn without requiring the two scans of the traditional measuring method (as shown in FIG. 1 B ) after the principal component analysis, and thus the present invention has a higher detection efficiency.
  • the probe 12 of the magnetic force microscope 10 of this embodiment is closer to the surface of the sample 9 (wherein the range of the measuring height is 0 ⁇ 50 nm), so that a larger magnetic force can be sensed to get a more accurate magnetic distribution image.
  • the magnetism of the probe 12 used in the embodiment as shown in FIG. 5 A is N
  • the magnetism of the probe 12 used in the embodiment as shown in FIG. 5 B is S.
  • the measured sense data are processed by the principal component analysis and found that the magnetic distribution of the first group of principal components falls within a height range of 50 ⁇ 250 nm, and the influence caused by the van der Waals force of the sample 9 on the probe 12 is smaller than the magnetic force of the sample 9 . It is known from FIGS. 5 A and 5 B that the magnetic distribution of the sample can be obtained by the detection method of the present invention, regardless of the magnetism (N or S) of the probe 12 .
  • this embodiment further adds the following steps S 6 ⁇ S 8 to the embodiment as shown in FIG. 2 A .
  • the steps S 6 ⁇ S 8 are elaborated as follows.
  • Step S 6 Cluster the magnetic distribution data by a clustering algorithm.
  • the clustering algorithm is a k-means clustering algorithm, which is one of the unsupervised machine learning methods, and this method can divide data into several types. Based on this principle, the k-means clustering algorithm can cluster the sense data of the magnetic distribution to generate a magnetic distribution image as shown in FIG. 7 . In this way, it will be conducive to further update and divide the magnetic distribution data more clearly in the future, so as to know the absolute magnetism direction of each magnetic domain on the sample, which is more convenient for some of the subsequent operations.
  • Step S 7 Select a plurality of measuring points 9 P of different magnetic areas on the surface of the sample 9 as shown in FIGS. 6 and 7 .
  • the measuring points 9 P 1 , 9 P 2 and 9 P 3 as shown in FIG. 7 are selected.
  • the measuring points 9 P 1 , 9 P 2 , and 9 P 3 are disposed in different magnetic areas respectively.
  • Step S 8 Analyze the relationship between the phase difference ⁇ and height of the selected measuring points 9 P 1 ⁇ 9 P 3 .
  • the phase difference ⁇ as described in the Step S 8 refers to the data obtained by the principal component analysis as described in the Step S 5 , which are the corresponding magnetic distribution data (excluding the corresponding data of the van der Waals force).
  • the relationship between the phase difference ⁇ and height of the measuring points 9 P 1 ⁇ 9 P 3 is represented by FIG. 8 . In FIG.
  • the relationship between the measured phase difference ⁇ and height of the measuring point 9 P 1 is that the phase difference ⁇ will increase with the decrease of the height;
  • the relationship between the measured phase difference ⁇ and height of the measuring point 9 P 2 is that the phase difference ⁇ will decrease with the decrease of the height;
  • the relationship between the measured phase difference ⁇ and height of the measuring point 9 P 3 is that the phase difference ⁇ basically will decrease with the decrease of the height, but the decrease of the phase difference ⁇ is smaller than that of the measuring point 9 P 2 .
  • the phase difference will decrease with the decrease of the height. Therefore, if the magnetism of the probe 12 is north pole, the magnetism of the area where the measuring point 9 P 1 is disposed will be north pole, and the magnetism of the area where the measuring point 9 P 2 is situated will be south pole, and the magnetism of the area where the measuring point 9 P 3 is situated can be regarded as a transitional area.
  • the magnetism of different magnetic areas on the surface of the sample 9 can be determined without requiring the magnetic reversal of the probe 12 . It is noteworthy that even without carrying out the Step S 6 , the Steps S 7 and S 8 can determine the magnetism of different magnetic areas on the surface of the sample 9 .
  • the main purpose of the clustering algorithm is to facilitate observer to clearly distinguish different magnetic areas on the sample, but it is not a necessary condition for the execution of the Steps S 7 and S 8 .
  • the weak magnetic sample of this embodiment is made of a cobalt ferrite film with ferromagnetism, and the magnetic force microscope 10 is used to measure the height of the measuring point (within the range of 50 ⁇ 250 nm) of the weak magnetic sample and obtain the magnetic distribution of the sample after the principal component analysis.
  • the sense data measured by the magnetic force microscope 10 are analyzed by the principal component analysis, the first group of principal components shows a magnetic force distribution.
  • the measurement of a weak magnetic sample will be interfered by the van der Waals force more easily, and it is not easy to obtain an accurate magnetic distribution.
  • the detection method of the present invention can still obtain the magnetic distribution of a weak magnetic sample accurately.
  • FIG. 9 B After the k-means clustering algorithm is used to process the data group of the first group of principal components, the magnetic distribution will be as shown in FIG. 9 B .
  • FIG. 9 A By comparing FIG. 9 A with FIG. 9 B , we can see that the observer can observe the absolute magnetic direction of each magnetic domain on the sample clearly after the processing by the k-means clustering algorithm.
  • the k-means clustering algorithm is an embodiment of the clustering algorithm.
  • the clustering algorithm can also be an agglomerative clustering algorithm, a spectral clustering algorithm, or a mean shift clustering algorithm.
  • the magnetic distribution detection method of the present invention has the following advantages:
  • this invention can eliminate the influence caused by the van der Waals forces without requiring two scans as required in the conventional method, thereby improving the detection efficiency.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • Measuring Magnetic Variables (AREA)
  • Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

A magnetic distribution detection method includes the steps of providing a magnetic sensor and a sample, selecting a multiple of measuring points on the sample, sensing the measuring points by the magnetic sensor, obtaining a multiple of sense data and a series of the heights of the magnetic sensor from each measuring point, using a signal decomposition algorithm to convert these sense data into data groups, and selecting one of the data groups as the magnetic distribution data of the sample.

Description

BACKGROUND OF THE INVENTION Field of the Invention
The present invention relates to a magnetic distribution detection method for detecting the magnetic distribution of a sample, particularly to the magnetic distribution detection method using machine learning.
Description of Related Art
Magnetic Force Microscope (MFM) is an atomic force microscope that uses a magnetic probe to scan a magnetic sample, and the interaction between a testing probe and the surface of a magnetic sample to measure the magnetic distribution or magnetic structure of the surface of the magnetic sample. However, the conventional magnetic force microscope still has the following shortcomings.
(1) In order to avoid the influence caused by the van der Waals force of the surface of the sample, it is necessary to take two measurements. In the first measurement, the measuring distance from the sample is closer for measuring the influence caused by the van der Waals force. In the second measurement, the measuring distance from the sample is farther, primarily for detecting the magnetic force. The results of the two measurements are combined to obtain the magnetic distribution of the surface of the sample. However, the measuring distance from the surface of the sample in the first measurement is closer, so that the result of the measurement is influenced by the van der Waals force significantly, and it is not easy to know about the magnetic force distribution at a closer distance. In the second measurement, the measuring distance from the surface of the sample is farther, the obtained magnetic signal is weaker because the magnetic force is inversely proportional to the square of distance and the spatial resolution of the magnetic distribution is limited because the diverging magnetic lines of each magnetic domain are mixed together.
(2) In order to confirm the magnetic property (i.e. the North Pole or the South Pole) of each magnetic domain on the surface of the sample, it is necessary to take another measurement after changing the magnetic pole of the probe, resulting in lowering the detection efficiency.
Therefore, how to design a highly efficient and accurate magnetic distribution detection method is worth consideration for a person having ordinary skill in the art.
SUMMARY OF THE INVENTION
It is a primary objective of the present invention to provide a magnetic distribution detection method with better detection efficiency and accuracy.
The magnetic distribution detection method of the present invention is used for detecting the magnetic distribution of a sample, and the method includes the steps of (a) providing a magnetic sensor, and aligning a probe of the magnetic sensor precisely with the sample; (b) selecting a plurality of measuring points on the sample; (c) sensing the measuring points by the magnetic sensor, and obtaining a plurality of sense data; (d) converting the sense data into a plurality of data groups by a signal decomposition algorithm; and (e) selecting one of the data groups as the magnetic distribution data of the sample. In the step (c), a plurality of heights (distances) from the measuring points is sensed, and the sense datum corresponding to each height is obtained.
In some embodiment, the magnetic sensor is a magnetic force microscope.
In some embodiment, the signal decomposition algorithm is a principal component analysis algorithm.
In some embodiment, after the step (e) the method further comprises the steps of (f) selecting a measuring point in a different magnetic area on the surface of the sample and (g) analyzing the relationship between the heights above the selected measuring points and the data obtained in the step (e). In addition, the step of clustering the magnetic distribution sense data by a clustering algorithm is carried out between the step (e) and the step (f).
In some embodiment, the clustering algorithm is a k-means clustering algorithm.
In some embodiment, the height has a range of 0˜50 nm or 50˜250 nm.
In some embodiment, the step of clustering the magnetic distribution sense data by a clustering algorithm is carried out after the step (e).
In some embodiment, the clustering algorithm is a k-means clustering algorithm.
In some embodiment, the sense data are represented by a phase difference.
Other and further features, advantages, and benefits of the invention will become apparent in the following description taken in conjunction with the following drawings. It is to be understood that the foregoing general description and following detailed description are exemplary and explanatory but are not to be restrictive of the invention. The accompanying drawings are incorporated in and constitute a part of this application and, together with the description, serve to explain the principles of the invention in general terms. Like numerals refer to like parts throughout the disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1A is a schematic view of a magnetic force microscope used in a magnetic distribution detection method in accordance with an embodiment of the present invention;
FIG. 1B is a schematic view of a probe measuring a sample at a higher position;
FIG. 2A is a flow chart of a magnetic distribution detection method of the present invention;
FIG. 3A is a top view of a sample;
FIG. 3B is a schematic view of the phase difference ΔØ and the height ΔZ when the distance of the magnetic probe from a surface of a sample 9 is the height ΔZ;
FIGS. 4A to 4C are pictures showing the force exertion distribution of the first to third data groups analyzed by principal component analysis respectively;
FIGS. 5A and 5B are pictures showing the magnetic distribution when the range of the heights above measuring points is set to 50˜250 nm for a magnetic force microscope;
FIG. 6 is a flow chart of a magnetic distribution detection method in accordance with another embodiment of the present invention;
FIG. 7 is a picture showing a pattern obtained after clustering computing;
FIG. 8 is a graph showing the relationship between phase difference ΔØ and height; and
FIGS. 9A and 9B are pictures showing the magnetic distribution of a weak magnetic sample.
 DESCRIPTION OF THE PREFERRED EMBODIMENTS
The objectives, technical characteristics and effects of the present invention will become apparent with the detailed description of preferred embodiments accompanied with the illustration of related drawings. It is intended that the embodiments and drawings disclosed herein are to be considered illustrative rather than restrictive.
With reference to FIG. 1A for a magnetic force microscope used in an embodiment of the present invention, the magnetic force microscope 10 includes a cantilever 11 and a probe 12, and the probe 12 is magnetic and installed at the front of the cantilever 11. The magnetic force microscope 10 uses the magnetic force between the probe 12 and a magnetic material (which is the sample 9) to measure the magnetic distribution of the sample 9 and then uses a computer system (not shown in the figure) to create an image of the magnetic distribution.
In a conventional detection, the probe 12 measures the sample 9 from a closer distance at first. The magnetic force microscope 10 generally needs to scan the sample 9 twice. In the first scan, the probe 12 measures the sample 9 from a closer distance (0˜50 nm) by a tapping mode, so that the influence to the cantilever 11 caused by the van der Waals force of the sample 9 is much greater than by the magnetic force, and thus the action of the cantilever 11 mainly reflects the appearance of the surface of the sample 9. For example, the surface of the sample 9 has a bump 91 and a groove 92, and thus the linear graph 11P showing the action of the cantilever 11 also reflects the appearance and shape of the bump 91 and the groove 92.
In order to reduce the influence caused by the van der Waals force, the probe 12 needs to keep a certain distance from the sample 9 to measure the magnetic signal of the sample 9 under the influence of the van der Waals force. Specifically, it is necessary to lift the probe 12 to a position with a distance D (such as 50˜250 nm) from the surface of the sample 9, and then carry out the second scan along the moving path of the first scan (please refer to FIG. 1B for a probe 12 measuring the sample 9 at a higher position). In this way, the force exertion conditions of the probe 12 obtained in the first scan and the second scan are taken into consideration to determine the magnetic distribution of sample 9. However, in the second scan, the probe 12 measures the sample 9 from a higher position, so that the received magnetic signal is weak and divergent, and the detection accuracy and the spatial resolution are poor.
In addition, if it is necessary to know the magnetism of each magnetic domain on the surface of the sample 9, the conventional test needs to reverse the original magnetic direction of the probe 12 (for example, the original north pole facing the sample is changed to the south pole facing the sample) and measure the same magnetic domain again in order to know the magnetism of each magnetic domain. However, it takes a lot of time for the reversal of the magnetism of the probe 12, leading to reduce the detection efficiency.
With reference to FIG. 2A for the flow chart of a magnetic distribution detection method in accordance with one of the embodiments of the present invention and FIG. 3A for the top view of the sample 9, the magnetic distribution detection method comprises the following steps:
Step S1: Provide a magnetic sensor and a sample 9. In this embodiment, the sample 9 is a magnetic tape; in other words, the magnetic area on the surface of the sample 9 shows a tape-shaped distribution. It is noteworthy that the magnetic area distribution on the sample 9 as depicted in FIG. 3A is illustrative only, but it does not represent the real situation. The magnetic sensor is the magnetic force microscope 10 as shown in FIG. 1A, and the material of the sample 9 is a ferromagnetic material with strong magnetism which is conducive to the observation of the magnetic distribution of the sample. In other embodiments, the magnetic sensor can also be a Scanning NV Magnetometry, a Scanning SQUID Microscope, or a Scanning Hall Probe Microscope (SHPM).
Step S2: Select a plurality of measuring points 9P on the sample 9, and the so-called measuring point 9P refers to a measuring point of the sample 9 in the magnetic force microscope 10, and the measuring points 9P is such as arranged in a matrix form. In FIG. 3A, only 64 measuring point 9P are shown, but more measuring points 9P, such as those arranged in a 128*128 matrix including 16384 measuring points are generally used. People having ordinary skill in the art should understand that the more the measuring points, the higher the resolution of the image of the magnetic distribution.
With reference to FIGS. 2A and 3B, FIG. 3B is the schematic view of the phase difference ΔØ and the height ΔZ when the distance of the magnetic probe from a surface of a sample 9 is the height ΔZ.
Step S3: Use a probe 12 of a magnetic force microscope 10 to obtain a plurality of sense data corresponding to a series of heights above the measuring points 9P respectively. In other words, the probe 12 of the magnetic force microscope 10 senses a plurality of heights above the measuring points 9P respectively and obtains the sense data (phase difference ΔØ) corresponding to the heights respectively. Please refer to FIG. 3A for details. When the sample 9 is measured, the probe 12 can stay at a position of a different height (within a range of 0˜250 nm) above each measuring point 9P for the sensing in order to obtain the phase difference ΔØ corresponding to the different height. For example, the range of the height from 0 nm to 250 nm can be divided into 256 points. In other words, the range of the height from 0 nm to 250 nm is divided into 256 equal parts, and the probe 12 is placed at each different height to measure the sample 9 and record the measurements. In this embodiment, the sample 9 is made of a ferromagnetic material having a plurality of magnetic domains. Since each magnetic domain has a different magnetic effect on the probe 12, the magnetic force microscope 10 can be used to obtain the relative magnetic properties of the magnetic domain.
In this embodiment, the sense data are represented by the phase difference ΔØ. The relationship between the phase difference ΔØ and the action force along the Z-axis of the probe 12 is shown below:
Δ ⁢ ∅ ≈ 2 ⁢ Q ⁢ Δ ⁢ ω ω 0 = - Q k ⁢ F z ′
In the above equation, Q is the quality factor, and k is the spring constant. Measuring the phase difference ΔØ is equivalent to measuring the exerted magnetic force. In the following embodiment, the phase difference ΔØ of each point on the same plane of the sample 9 is expressed by brightness and contrast and used to express the magnetic distribution of the surface of the sample 9. However, the phase difference ΔØ of each point on the sample 9 can also be expressed by a 3D drawing. In other words, the Z-axis represents the phase difference ΔØ, the XY plane represents the surface of the sample 9. In addition, the force exertion of the probe 12 can also be represented by frequency or amplitude. In the following embodiments, all sense data are represented by the phase difference ΔØ.
In this embodiment, the range of the height above the measuring point 9P of the probe 12 of the magnetic force microscope 10 is set to 0˜50 nm. Since the magnetic force is inversely proportional to the square of distance, therefore the magnetic force microscope 10 is closer to the sample 9 within this range, and the phase difference ΔØ can be measured more precisely. Of course, the distance between the probe 12 of the magnetic force microscope 10 and the sample 9 can have another height range such as 50˜250 nm.
Step S4: Use a signal decomposition algorithm to convert the sense data (or phase difference ΔØ) into a plurality of data groups. Specifically, the signal decomposition algorithm used in this embodiment is a principal component analysis algorithm (PCA) which is a data dimension reduction analysis technique. In other words, the principal component analysis algorithm will convert all sense data into a plurality of data groups which are independent to one another in vector space.
With reference to FIGS. 4A to 4C for the pictures showing the force exertion distribution of the first to third data groups analyzed by principal component analysis respectively, the principal component analysis used in this embodiment converts all sense data into multiple data groups. In the embodiments, the number of data groups is equal to the number of height divisions (e.g. 256 points). By a computer system three diagrams of the force exertion distribution are formed according to the first to third data groups (the force exertion distribution of other groups are not shown in the embodiment). In the principal component analysis, the smaller the labelled number the principal component, the larger the representativeness to the sense data. For example, the first group of principal components has a percentage of impact higher than that of the second groups of principal components, and the second group of principal components has a percentage of impact higher than that of the third group of principal components. In other embodiments, other types of signal decomposition algorithm can also convert the sense data into the plurality of data groups, in which there is one of the groups that represents the influence caused by the magnetic force.
In the above embodiment, the principal component analysis algorithm is used as the signal decomposition algorithm. However, other types of signal decomposition algorithm, such as non-negative matrix factorization, independent components analysis, or factor analysis can also be used in other embodiments of the present invention. The reason why the principal component analysis algorithm or other signal decomposition algorithms can be used to convert the measured signal into different components resides on the sense data of at a series of heights above the measuring points 9P as described in the Step S3. Since the change of van der Waals force varying with the height and the change of the magnetic force varying with the height are different, therefore the principal component analysis can convert the van der Waals force and the magnetic force in the sense data into different data groups.
Step S5: Select one of the data groups as the magnetic distribution data of the sample. In detail, the sample 9 of this embodiment is a magnetic tape made of a ferromagnetic material. Since the sample 9 is a magnetic tape, the magnetic distribution will be in the shape of a tape, and the first group of principal components as shown in FIG. 4A are able to be determined as being contributed by magnetism. Since the magnetic distribution of the magnetic tape can be expected to be in the shape of a tape, therefore in the Step S5 a computer with function or algorithm for pattern recognition is used for determination in some embodiment. With reference to FIGS. 4B and 4C for the images of the second groups of principal components and the third groups of principal components respectively, FIGS. 4B and 4C show the surface undulation and the left half side of the particles on the surface, respectively.
From the description above, even though the probe 12 of the magnetic force microscope 10 is closer to the surface of the sample 9 and the detection result is affected by the van der Waals force more easily, one of the groups (which is the first group of principal components of this embodiment) can be used as the magnetic distribution and the magnetic distribution chart can be drawn without requiring the two scans of the traditional measuring method (as shown in FIG. 1B) after the principal component analysis, and thus the present invention has a higher detection efficiency. Compared with the prior art, the probe 12 of the magnetic force microscope 10 of this embodiment is closer to the surface of the sample 9 (wherein the range of the measuring height is 0˜50 nm), so that a larger magnetic force can be sensed to get a more accurate magnetic distribution image.
With reference to FIGS. 5A and 5B for the pictures showing the magnetic distribution when the range of the heights above measuring points is set to 50˜250 nm for a magnetic force microscope respectively, the magnetism of the probe 12 used in the embodiment as shown in FIG. 5A is N, and the magnetism of the probe 12 used in the embodiment as shown in FIG. 5B is S. In the embodiment as shown in FIGS. 5A and 5B, the measured sense data are processed by the principal component analysis and found that the magnetic distribution of the first group of principal components falls within a height range of 50˜250 nm, and the influence caused by the van der Waals force of the sample 9 on the probe 12 is smaller than the magnetic force of the sample 9. It is known from FIGS. 5A and 5B that the magnetic distribution of the sample can be obtained by the detection method of the present invention, regardless of the magnetism (N or S) of the probe 12.
With reference to FIG. 6 for the flow chart of a magnetic distribution detection method in accordance with another embodiment of the present invention, this embodiment further adds the following steps S6˜S8 to the embodiment as shown in FIG. 2A. The steps S6˜S8 are elaborated as follows.
Step S6: Cluster the magnetic distribution data by a clustering algorithm. In other words, after one of the data groups is selected as the magnetic distribution data of the sample after the principal component analysis in Step S5, the magnetic distribution data are clustered. In the embodiment, the clustering algorithm is a k-means clustering algorithm, which is one of the unsupervised machine learning methods, and this method can divide data into several types. Based on this principle, the k-means clustering algorithm can cluster the sense data of the magnetic distribution to generate a magnetic distribution image as shown in FIG. 7 . In this way, it will be conducive to further update and divide the magnetic distribution data more clearly in the future, so as to know the absolute magnetism direction of each magnetic domain on the sample, which is more convenient for some of the subsequent operations.
Step S7: Select a plurality of measuring points 9P of different magnetic areas on the surface of the sample 9 as shown in FIGS. 6 and 7 . In this embodiment, the measuring points 9P1, 9P2 and 9P3 as shown in FIG. 7 are selected. In FIG. 7 , the measuring points 9P1, 9P2, and 9P3 are disposed in different magnetic areas respectively.
Step S8: Analyze the relationship between the phase difference ΔØ and height of the selected measuring points 9P1˜9P3. It is noteworthy that the phase difference ΔØ as described in the Step S8 refers to the data obtained by the principal component analysis as described in the Step S5, which are the corresponding magnetic distribution data (excluding the corresponding data of the van der Waals force). In this embodiment, the relationship between the phase difference ΔØ and height of the measuring points 9P1˜9P3 is represented by FIG. 8 . In FIG. 8 , the relationship between the measured phase difference ΔØ and height of the measuring point 9P1 is that the phase difference ΔØ will increase with the decrease of the height; the relationship between the measured phase difference ΔØ and height of the measuring point 9P2 is that the phase difference ΔØ will decrease with the decrease of the height; and the relationship between the measured phase difference ΔØ and height of the measuring point 9P3 is that the phase difference ΔØ basically will decrease with the decrease of the height, but the decrease of the phase difference ΔØ is smaller than that of the measuring point 9P2. Theoretically, if the magnetism of the probe 12 and the magnetism of the surface of the sample 9 are the same (in other words, a repulsion exists between them), the phase difference will increase with the decrease of the height. On the contrary, if the magnetism of the probe 12 and the magnetism of the surface of the sample 9 are different (in other words, an attraction exists between them), the phase difference will decrease with the decrease of the height. Therefore, if the magnetism of the probe 12 is north pole, the magnetism of the area where the measuring point 9P1 is disposed will be north pole, and the magnetism of the area where the measuring point 9P2 is situated will be south pole, and the magnetism of the area where the measuring point 9P3 is situated can be regarded as a transitional area.
By the method as described in the Steps S7 and S8, the magnetism of different magnetic areas on the surface of the sample 9 can be determined without requiring the magnetic reversal of the probe 12. It is noteworthy that even without carrying out the Step S6, the Steps S7 and S8 can determine the magnetism of different magnetic areas on the surface of the sample 9. In other words, the main purpose of the clustering algorithm is to facilitate observer to clearly distinguish different magnetic areas on the sample, but it is not a necessary condition for the execution of the Steps S7 and S8.
With reference to FIGS. 9A and 9B for the magnetic distribution of a weak magnetic sample, the weak magnetic sample of this embodiment is made of a cobalt ferrite film with ferromagnetism, and the magnetic force microscope 10 is used to measure the height of the measuring point (within the range of 50˜250 nm) of the weak magnetic sample and obtain the magnetic distribution of the sample after the principal component analysis. In FIG. 9A, the sense data measured by the magnetic force microscope 10 are analyzed by the principal component analysis, the first group of principal components shows a magnetic force distribution. In the prior art, the measurement of a weak magnetic sample will be interfered by the van der Waals force more easily, and it is not easy to obtain an accurate magnetic distribution. However, the detection method of the present invention can still obtain the magnetic distribution of a weak magnetic sample accurately.
After the k-means clustering algorithm is used to process the data group of the first group of principal components, the magnetic distribution will be as shown in FIG. 9B. By comparing FIG. 9A with FIG. 9B, we can see that the observer can observe the absolute magnetic direction of each magnetic domain on the sample clearly after the processing by the k-means clustering algorithm.
In the embodiment above, the k-means clustering algorithm is an embodiment of the clustering algorithm. In other embodiments, the clustering algorithm can also be an agglomerative clustering algorithm, a spectral clustering algorithm, or a mean shift clustering algorithm.
In summation of the description above, the magnetic distribution detection method of the present invention has the following advantages:
(1) By using the principal component analysis or other types of signal decomposition algorithms, this invention can eliminate the influence caused by the van der Waals forces without requiring two scans as required in the conventional method, thereby improving the detection efficiency.
(2) There is no need to change the magnetic poles of the probe in order to confirm the magnetic property (i.e. North Pole or South Pole) of each magnetic domain on the surface of the sample.
(3) By using the k-means clustering algorithm or other clustering algorithms, the observer can observe the absolute magnetic direction of each magnetic domain on the sample clearly.
Although the invention has been disclosed and illustrated with reference to particular embodiments, the principles involved are susceptible for use in numerous other embodiments that will be apparent to persons skilled in the art. This invention is, therefore, to be limited only as indicated by the scope of the appended claims.

Claims (18)

What is claimed is:
1. A magnetic distribution detection method, for detecting the magnetic distribution of a sample, and the method comprising the steps of:
(a) providing a magnetic sensor, and precisely aligning a probe of the magnetic sensor with the sample;
(b) selecting a plurality of measuring points on the sample;
(c) sensing the measuring points by the magnetic sensor, and obtaining a plurality of sense data;
(d) converting the sense data into a plurality of data groups by a signal decomposition algorithm; and
(e) selecting one of the data groups as the magnetic distribution data of the sample;
wherein in the step (c), a plurality of heights from the measuring points are sensed, and the sense datum corresponding to each height is obtained.
2. The magnetic distribution detection method according to claim 1, wherein the magnetic sensor is a magnetic force microscope.
3. The magnetic distribution detection method according to claim 1, wherein the signal decomposition algorithm is a principal component analysis algorithm.
4. The magnetic distribution detection method according to claim 1, after the step (e) the method further comprising the steps of:
(f) selecting a measuring point in a different magnetic area on the surface of the sample; and
(g) analyzing the relationship between the heights above the selected measuring point and the data obtained in the step (e).
5. The magnetic distribution detection method according to claim 4, further comprising the step of clustering the magnetic distribution sense data by a clustering algorithm, which is carried out between the step (e) and the step (f).
6. The magnetic distribution detection method according to claim 5, wherein the clustering algorithm is a k-means clustering algorithm.
7. The magnetic distribution detection method according to claim 1, wherein the height has a range of 0˜50 nm or 50˜250 nm.
8. The magnetic distribution detection method according to claim 1, further comprising the step of clustering the magnetic distribution sense data by a clustering algorithm, which is carried out after the step (e).
9. The magnetic distribution detection method according to claim 8, wherein the clustering algorithm is a k-means clustering algorithm.
10. The magnetic distribution detection method according to claim 1, wherein the sense data are represented by a phase difference.
11. The magnetic distribution detection method according to claim 2, wherein the signal decomposition algorithm is a principal component analysis algorithm.
12. The magnetic distribution detection method according to claim 2, after the step (e) the method further comprising the steps of:
(f) selecting a measuring point in a different magnetic area on the surface of the sample; and
(g) analyzing the relationship between the heights above the selected measuring point and the data obtained in the step (e).
13. The magnetic distribution detection method according to claim 12, further comprising the step of clustering the magnetic distribution sense data by a clustering algorithm, which is carried out between the step (e) and the step (f).
14. The magnetic distribution detection method according to claim 13, wherein the clustering algorithm is a k-means clustering algorithm.
15. The magnetic distribution detection method according to claim 2, wherein the height has a range of 0˜50 nm or 50˜250 nm.
16. The magnetic distribution detection method according to claim 2, further comprising the step of clustering the magnetic distribution sense data by a clustering algorithm, which is carried out after the step (e).
17. The magnetic distribution detection method according to claim 16, wherein the clustering algorithm is a k-means clustering algorithm.
18. The magnetic distribution detection method according to claim 2, wherein the sense data are represented by a phase difference.
US17/585,570 2021-09-15 2022-01-27 Magnetic distribution detection method Active 2042-01-27 US11719766B2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
TW110134456A TWI783684B (en) 2021-09-15 2021-09-15 Method of detecting magnetic distribution
TW110134456 2021-09-15

Publications (2)

Publication Number Publication Date
US20230078584A1 US20230078584A1 (en) 2023-03-16
US11719766B2 true US11719766B2 (en) 2023-08-08

Family

ID=85479007

Family Applications (1)

Application Number Title Priority Date Filing Date
US17/585,570 Active 2042-01-27 US11719766B2 (en) 2021-09-15 2022-01-27 Magnetic distribution detection method

Country Status (2)

Country Link
US (1) US11719766B2 (en)
TW (1) TWI783684B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116165583B (en) * 2023-04-20 2023-08-04 常州市华星机械有限公司 Magnetic control wheel detection device of magnetic resistor

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6059718A (en) * 1993-10-18 2000-05-09 Olympus Optical Co., Ltd. Endoscope form detecting apparatus in which coil is fixedly mounted by insulating member so that form is not deformed within endoscope
US6337477B1 (en) * 1998-07-16 2002-01-08 Canon Kabushiki Kaisha Probe having micro-projection and manufacturing method thereof
US20130196593A1 (en) * 2010-07-16 2013-08-01 Michael Roper Portable through-the-earth radio
US9478393B2 (en) 2014-06-30 2016-10-25 Fei Company Computational scanning microscopy with improved resolution
US9535087B2 (en) 2006-09-01 2017-01-03 Ut-Battelle, Llc Band excitation method applicable to scanning probe microscopy
US9612257B1 (en) 2016-03-07 2017-04-04 Ut-Battelle, Llc Full information acquisition in scanning probe microscopy and spectroscopy
US10295617B2 (en) 2013-02-25 2019-05-21 Kenjiro Kimura Distribution analyzing device and distribution analyzing method
US10573488B2 (en) 2015-04-15 2020-02-25 Fei Company Method of performing tomographic imaging in a charged-particle microscope
CN111337710A (en) 2020-02-20 2020-06-26 罗浩维 Piezoelectric force microscope data reading and measuring system, device and method
US11132637B1 (en) * 2016-09-20 2021-09-28 Amazon Technologies, Inc. System to detect user interaction with inventory
US20220003798A1 (en) 2018-11-07 2022-01-06 Trustees Of Tufts College Atomic-Force Microscopy for Identification of Surfaces

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6059718A (en) * 1993-10-18 2000-05-09 Olympus Optical Co., Ltd. Endoscope form detecting apparatus in which coil is fixedly mounted by insulating member so that form is not deformed within endoscope
US6337477B1 (en) * 1998-07-16 2002-01-08 Canon Kabushiki Kaisha Probe having micro-projection and manufacturing method thereof
US9535087B2 (en) 2006-09-01 2017-01-03 Ut-Battelle, Llc Band excitation method applicable to scanning probe microscopy
US20130196593A1 (en) * 2010-07-16 2013-08-01 Michael Roper Portable through-the-earth radio
US10295617B2 (en) 2013-02-25 2019-05-21 Kenjiro Kimura Distribution analyzing device and distribution analyzing method
US9478393B2 (en) 2014-06-30 2016-10-25 Fei Company Computational scanning microscopy with improved resolution
US10573488B2 (en) 2015-04-15 2020-02-25 Fei Company Method of performing tomographic imaging in a charged-particle microscope
US9612257B1 (en) 2016-03-07 2017-04-04 Ut-Battelle, Llc Full information acquisition in scanning probe microscopy and spectroscopy
US11132637B1 (en) * 2016-09-20 2021-09-28 Amazon Technologies, Inc. System to detect user interaction with inventory
US20220003798A1 (en) 2018-11-07 2022-01-06 Trustees Of Tufts College Atomic-Force Microscopy for Identification of Surfaces
CN111337710A (en) 2020-02-20 2020-06-26 罗浩维 Piezoelectric force microscope data reading and measuring system, device and method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Agar et al., "Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.803 Thin Films", Advaced Materials, 2018.
Collins et al., "G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics", Applied Physics Letters, May 9, 2016.

Also Published As

Publication number Publication date
TW202314252A (en) 2023-04-01
TWI783684B (en) 2022-11-11
US20230078584A1 (en) 2023-03-16

Similar Documents

Publication Publication Date Title
Sherrington et al. Modern measurement techniques in surface metrology: part I; stylus instruments, electron microscopy and non-optical comparators
Min et al. Strain measurement during tensile testing using deep learning-based digital image correlation
Yuan et al. Lattice strain mapping using circular Hough transform for electron diffraction disk detection
CN111522835A (en) A multi-magnetic target location detection method based on database feature matching
CN109270079A (en) A kind of Surface Flaw accurate detecting method based on point cloud model
US11719766B2 (en) Magnetic distribution detection method
Chang et al. Deep-learning-based automated morphology analysis with atomic force microscopy
Fedotkin et al. Automatic processing of microhardness images using computer vision methods
US11170972B2 (en) Scanning electron microscope and method for analyzing secondary electron spin polarization
Pavlyuchenko et al. Pulsed Magnetic Field near Metal Surface
Yao et al. A hardness measuring method based on hough fuzzy vertex detection algorithm
US12123895B2 (en) Method of determining dimensions of features of a subsurface topography, scanning probe microscopy system and computer program
US6486654B1 (en) Calibration of magnetic force or scanning hall probe microscopes by measuring sample images and calculating instrument response functions
JPH1038984A (en) Failure site detection method and device
JP4424065B2 (en) Measuring method using scanning probe microscope
Li et al. Three-Dimensional Reconstruction of Complex Defects in Thin Steel Strips with the Imaging Method of Magnetic Field Distortion
CN116399938B (en) A method for processing stress-defect aliased magnetic signals
US20250189303A1 (en) Method for combining height maps and profilometer for the same
US20250044377A1 (en) Image output apparatus, method, program, and recording medium
Cochran et al. Recent progress in SQUIDs as sensors for electromagnetic NDE
Moussa et al. Localized power spectral density analysis on atomic force microscopy images for advanced patterning applications
CN119379763B (en) Structural displacement video measurement method, system and medium based on phase cross correlation
EP3943923B1 (en) Method and apparatus for image processing
Jiang et al. Recent progress on the study of the transfer standard for micro/nano Newton force measurement in NIM, China
Wan Vibration-Based Damage Detection Using High-speed Camera

Legal Events

Date Code Title Description
FEPP Fee payment procedure

Free format text: ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY

FEPP Fee payment procedure

Free format text: ENTITY STATUS SET TO SMALL (ORIGINAL EVENT CODE: SMAL); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

AS Assignment

Owner name: NATIONAL CHENG KUNG UNIVERSITY, TAIWAN

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:CHEN, YI-CHUN;LIOU, YI-DE;HUANG, GUO-WEI;REEL/FRAME:059216/0049

Effective date: 20220113

STPP Information on status: patent application and granting procedure in general

Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED

STCF Information on status: patent grant

Free format text: PATENTED CASE